pacman::p_load(tmap, sf, DT, stplanr,
performance,
ggpubr, tidyverse)Hands-on Exercise 2:Processing and Visualising Flow Data
1 Overview
In this hands-on exercise, you will learn how to build an OD matrix by using Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall.
2 Getting Started
For the purpose of this exercise, four r packages will be used. They are:
sf for importing, integrating, processing and transforming geospatial data.
tidyverse for importing, integrating, wrangling and visualising data.
tmap for creating thematic maps.
3 Preparing the Flow Data
3.1 Importing the OD data
Firstly, we will import the Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall by using read_csv() of readr package.
odbus <- read_csv("data/origin_destination_bus_202310.csv")Rows: 5694297 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): YEAR_MONTH, DAY_TYPE, PT_TYPE, ORIGIN_PT_CODE, DESTINATION_PT_CODE
dbl (2): TIME_PER_HOUR, TOTAL_TRIPS
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <chr> "04168", "04168", "80119", "80119", "44069", "2028…
$ DESTINATION_PT_CODE <chr> "10051", "10051", "90079", "90079", "17229", "2014…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…A quick check of odbus tibble data frame shows that the values in OROGIN_PT_CODE and DESTINATON_PT_CODE are in numeric data type. Hence, the code chunk below is used to convert these data values into character data type.
odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE)3.2 Extracting the study data
For the purpose of this exercise, we will extract commuting flows on weekday and between 6 and 9 o’clock.
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 &
TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE,
DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))`summarise()` has grouped output by 'ORIGIN_PT_CODE'. You can override using
the `.groups` argument.Table below shows the content of odbus6_9
datatable(odbus6_9)Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlSave the output in rds format for future used.
write_rds(odbus6_9, "data/rds/odbus6_9.rds")Import the save odbus6_9.rds into R environment.
odbus6_9 <- read_rds("data/rds/odbus6_9.rds")4 Working with Geospatial Data
For the purpose of this exercise, two geospatial data will be used. They are:
BusStop: This data provides the location of bus stop as at last quarter of 2022.
MPSZ-2019: This data provides the sub-zone boundary of URA Master Plan 2019.
4.1 Importing geospatial data
Two geospatial data will be used in this exercise, they are:
busstop <- st_read(dsn = "data/geospatial/",
layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`/Users/WangYuhui/Desktop/SMU/Special_Term/ISSS624-G1-Applied-Geospatial-Analytics/ISSS624/Hands-on_Ex_3/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21